如何绘制样本的PMF?

问题描述

是否有任何函数或库可以帮助我绘制样本的概率质量函数，就像绘制样本的概率密度函数一样?

Is there any function or library that would help me to plot a probability mass function of a sample the same way there is for plotting the probability density function of a sample ?

例如，使用熊猫，绘制PDF就像调用一样简单:

For instance, using pandas, plotting a PDF is as simple as calling:

sample.plot(kind="density")

如果没有简便的方法，我该如何计算PMF，以便可以使用matplotlib进行绘图?

If there is no easy way, how can I compute the PMF so I could plot using matplotlib ?

推荐答案

如果ts是一个序列，则可以通过以下方式获得样本的PMF:

If ts is a series, you may obtain PMF of the sample by:

>>> pmf = ts.value_counts().sort_index() / len(ts)

并通过以下方式进行绘制:

and plot it by:

>>> pmf.plot(kind='bar')

---

仅numpy解决方案可以使用 np.unique :

---

numpy only solution can be done using np.unique:

>>> xs = np.random.randint(0, 10, 100)
>>> xs
array([5, 2, 2, 1, 2, 8, 6, 7, 5, 3, 2, 6, 4, 9, 7, 6, 4, 7, 6, 8, 7, 0, 6,
       2, 9, 8, 7, 7, 2, 6, 2, 8, 0, 2, 5, 1, 3, 6, 7, 7, 2, 2, 0, 3, 8, 7,
       4, 0, 5, 7, 5, 4, 4, 9, 5, 1, 6, 6, 0, 9, 4, 2, 0, 8, 7, 5, 1, 1, 2,
       8, 3, 8, 9, 0, 0, 6, 8, 7, 2, 6, 7, 9, 7, 8, 8, 3, 3, 7, 8, 2, 2, 4,
       4, 5, 3, 4, 1, 5, 5, 1])

>>> val, cnt = np.unique(xs, return_counts=True)
>>> pmf = cnt / len(xs)

>>> # values along with probability mass function
>>> np.column_stack((val, pmf))
array([[ 0.  ,  0.08],
       [ 1.  ,  0.07],
       [ 2.  ,  0.15],
       [ 3.  ,  0.07],
       [ 4.  ,  0.09],
       [ 5.  ,  0.1 ],
       [ 6.  ,  0.11],
       [ 7.  ,  0.15],
       [ 8.  ,  0.12],
       [ 9.  ,  0.06]])

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